Geospatial modeling system providing user-selectable building shape options and related methods

ABSTRACT

A geospatial modeling system may include a geospatial model data storage device, a user input device, and a display. A processor may be included for cooperating with the geospatial model data storage device, the user input device and the display for displaying a geospatial model data set on the display including at least one group of building data points, and displaying a plurality of user-selectable different building shapes on the display based upon the at least one group of building data points. The plurality of user-selectable different building shapes may have different respective feature detail levels. The processor may further replace the at least one group of building data points with a given one of the user-selectable different building shapes based upon user selection thereof with the user input device.

FIELD OF THE INVENTION

The present invention relates to the field of data modeling, and, moreparticularly, to modeling systems such as geospatial modeling systemsand related methods.

BACKGROUND OF THE INVENTION

Topographical models of geographical areas may be used for manyapplications. For example, topographical models may be used in flightsimulators and for planning military missions. Furthermore,topographical models of man-made structures (e.g., cities) may beextremely helpful in applications such as cellular antenna placement,urban planning, disaster preparedness and analysis, and mapping, forexample.

Various types and methods for making topographical models are presentlybeing used. One common topographical model is the digital elevation map(DEM). A DEM is a sampled matrix representation of a geographical areawhich may be generated in an automated fashion by a computer. In a DEM,coordinate points are made to correspond with a height value. DEMs aretypically used for modeling terrain where the transitions betweendifferent elevations (e.g., valleys, mountains, etc.) are generallysmooth from one to a next. That is, DEMs typically model terrain as aplurality of curved surfaces and any discontinuities therebetween arethus “smoothed” over. Thus, in a typical DEN no distinct objects arepresent on the terrain.

One particularly advantageous 3D site modeling product is RealSite® fromthe present Assignee Harris Corp. RealSite® may be used to registeroverlapping images of a geographical area of interest, and extract highresolution DEMs using stereo and nadir view techniques. RealSite®provides a semi-automated process for making three-dimensional (3D)topographical models of geographical areas, including cities, that haveaccurate textures and structure boundaries. Moreover, RealSite® modelsare geospatially accurate. That is, the location of any given pointwithin the model corresponds to an actual location in the geographicalarea with very high accuracy. The data used to generate RealSite® modelsmay include aerial and satellite photography, electro-optical, infrared,and light detection and ranging (LIDAR), for example.

Another similar system from Harris Corp. is LiteSite®. LiteSite® modelsprovide automatic extraction of ground, foliage, and urban digitalelevation models (DEMs) from LIDAR and IFSAR imagery. LiteSite™ can beused to produce affordable, geospatially accurate, high-resolution 3-Dmodels of buildings and terrain.

U.S. Pat. No. 6,654,690 to Rahmes et al., which is also assigned to thepresent Assignee and is hereby incorporated herein in its entirety byreference, discloses an automated method for making a topographicalmodel of an area including terrain and buildings thereon based uponrandomly spaced data of elevation versus position. The method includesprocessing the randomly spaced data to generate gridded data ofelevation versus position conforming to a predetermined position grid,processing the gridded data to distinguish building data from terraindata, and performing polygon extraction for the building data to makethe topographical model of the area including terrain and buildingsthereon.

In many instances there will be voids or gaps in the data used togenerate a geospatial or other model. The voids negatively affect thequality of the resulting model, and thus it is desirable to compensatefor these voids while processing the data, if possible. Variousinterpolation techniques are generally used for filling in missing datain a data field. One such technique is sinc interpolation, which assumesthat a signal is band-limited. While this approach is well suited forcommunication and audio signals, it may not be well suited for 3D datamodels. Another approach is polynomial interpolation. This approach issometimes difficult to implement because the computational overhead maybecome overly burdensome for higher order polynomials, which may benecessary to provide desired accuracy.

One additional interpolation approach is spline interpolation. Whilethis approach may provide a relatively high reconstruction accuracy,this approach may be problematic to implement in a 3D data model becauseof the difficulty in solving a global spline over the entire model, andbecause the required matrices may be ill-conditioned. One furtherdrawback of such conventional techniques is that they tend to blur edgecontent, which may be a significant problem in a 3D topographical model.

Another approach for filling in regions within an image is set forth inU.S. Pat. No. 6,987,520 to Criminisi et al. This patent discloses anexemplar-based filling system which identifies appropriate fillingmaterial to replace a destination region in an image and fills thedestination region using this material. This is done to alleviate orminimize the amount of manual editing required to fill a destinationregion in an image. Tiles of image data are “borrowed” from theproximity of the destination region or some other source to generate newimage data to fill in the region. Destination regions may be designatedby user input (e.g., selection of an image region by a user) or by othermeans (e.g., specification of a color or feature to be replaced). Inaddition, the order in which the destination region is filled by exampletiles may be configured to emphasize the continuity of linear structuresand composite textures using a type of isophote-driven image-samplingprocess.

With respect to geospatial models such as DEMs, various approaches havebeen attempted to address error recognition and correction due to voids,etc. One such approach is set forth in an article by Gousie entitled“Digital Elevation Model Error Detection and Visualization,” 4th ISPRSWorkshop on Dynamic & Multi-dimensional GIS (Pontypridd, Wales, UK,2005), C. Gold, Ed., pp. 42-46. This paper presents two methods forvisualizing errors in a DEM. One method begins with a root mean squareerror (RMSE) and then highlights areas in the DEM that contain errorsbeyond a threshold. A second method computes local curvature anddisplays discrepancies in the DEM. The visualization methods are inthree dimensions and are dynamic, giving the viewer the option ofrotating the surface to inspect any portion at any angle.

Another example is set forth in an article by Grohman et al. entitled“Filling SRTM Voids: The Delta Surface Fill Method,” PhotogrammetricEngineering & Remote Sensing, March 2006, pp. 213-216. This articlediscusses a technique for fillings voids in SRTM digital elevation datais that is intended to provide an improvement over traditionalapproaches, such as the Fill and Feather (F&F) method. In the F&Fapproach, a void is replaced with the most accurate digital elevationsource (“fill”) available with the void-specific perimeter bias removed.Then the interface is feathered into the SRTM, smoothing the transitionto mitigate any abrupt change. It works optimally when the two surfacesare very close together and separated by only a bias with minimaltopographic variance. The Delta Surface Fill (DSF) process replaces thevoid with fill source posts that are adjusted to the SRTM values foundat the void interface. This process causes the fill to more closelyemulate the original SRTM surface while still retaining the useful datathe fill contains.

Despite the advantages such prior art approaches may provide in certainapplications, further advancements may be desirable for error detectionand correction in geospatial and other model data.

SUMMARY OF THE INVENTION

In view of the foregoing background, it is therefore an object of thepresent invention to provide a geospatial modeling system with enhancedfeature rendering options and related methods.

This and other objects, features, and advantages are provided by ageospatial modeling system which may include a geospatial model datastorage device, a user input device, and a display. Moreover, aprocessor may be included for cooperating with the geospatial model datastorage device, the user input device and the display for displaying ageospatial model data set on the display including at least one group ofbuilding data points, and displaying a plurality of user-selectabledifferent building shapes on the display based upon the at least onegroup of building data points. More particularly, the plurality ofuser-selectable different building shapes may have different respectivefeature detail levels. The processor may further replace the at leastone group of building data points with a given one of theuser-selectable different building shapes based upon user selectionthereof with the user input device.

The processor may also update the geospatial model data based upon theuser selection of the given one of the user-selectable differentbuilding shapes. The plurality of user-selectable different buildingshapes may include a generally rectangular building box as a lowestfeature detail level, and/or a match of the at least one group ofbuilding data points as a highest feature detail level. Moreover, theplurality of user-selectable different building shapes may furtherinclude a plurality of user-selectable building shapes having respectivedifferent intermediate feature detail levels between the lowest andhighest feature detail levels.

Additionally, the processor may further select the at least one group ofbuilding points for displaying from among a plurality of groups thereofbased upon error calculations. By way of example, the error calculationsmay comprise at least one root mean square error (RMSE) calculation.Moreover, the error calculations may comprise two-dimensional and/orthree-dimensional error calculation.

A geospatial modeling method aspect may include displaying a geospatialmodel data set on a display including at least one group of buildingdata points, and displaying a plurality of user-selectable differentbuilding shapes on the display based upon the at least one group ofbuilding data points. The plurality of user-selectable differentbuilding shapes may have different respective feature detail levels. Themethod may further include replacing the at least one group of buildingdata points with a given one of the user-selectable different buildingshapes based upon user selection thereof with a user input device.

A computer-readable medium is also provided which may have computerexecutable instructions for causing a computer to perform stepscomprising displaying a geospatial model data set on a display includingat least one group of building data points, and displaying a pluralityof user-selectable different building shapes on the display based uponthe at least one group of building data points. As noted above, theplurality of user-selectable different building shapes may havedifferent respective feature detail levels. A further step may includereplacing the at least one group of building data points with a givenone of the user-selectable different building shapes based upon userselection thereof with a user input device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a geospatial modeling system inaccordance with one exemplary embodiment.

FIGS. 2 and 3 are a digital elevation model (DEM) and a correspondingerror DEM for which the system of FIG. 1 performs error detection andcorrection.

FIG. 4 is a screen print of the error DEM of FIG. 3 and an associatedtable identifying relative errors of localized error regions.

FIGS. 5A and 5B are triangulated irregular network (TIN) representationsof the DEM of FIG. 2 with and without error indicating boundariesindicating localized error regions, respectively.

FIGS. 6A and 6B are more detailed views of a portion of the TINs ofFIGS. 5A and 5B, respectively.

FIGS. 7 and 8 are flow diagrams illustrating a geospatial modelingmethod for identifying and inpainting localized error regions ingeospatial model data.

FIG. 9 is a schematic block diagram of an alternative embodiment of thesystem of FIG. 1 providing user-selectable building shape options.

FIG. 10 is a series of building shapes displayed by the system of FIG.9.

FIGS. 11 and 12 are flow diagrams illustrating an alternative geospatialmodeling method for providing the user-selectable building shapeoptions.

FIG. 13 is a schematic block diagram of yet another alternativeembodiment of the system of FIG. 1 for generating building shapes basedupon user-selected building areas and determined height values.

FIG. 14 is a screen print of a DEM with a building boundary area to bereplaced with a generated building shape.

FIG. 15 is a comparison of screen prints for the DEN of FIG. 14 afterfully automatic generation, and after being touched up through amanual/automated approach by the system of FIG. 13.

FIGS. 16A and 16B are 3D display views of the DEMs of FIG. 15 for thefully automatic and the touched-up versions, respectively.

FIG. 17 is a flow diagram of another alternative embodiment of ageospatial modeling method for generating building shapes based uponuser-selected building areas and determined height values.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Likenumbers refer to like elements throughout, and prime notation is used toindicate similar elements in alternate embodiments.

Referring initially to FIGS. 1-7, a geospatial modeling system 30illustratively includes a geospatial model data storage device 31, aprocessor 32, and (optionally) a display 33. The geospatial model datastorage device 31 stores geospatial model data, such as digitalelevation model (DEN), digital surface model (DSM), and/or triangulatedirregular network (TTIN) data, for example. Generally speaking, suchmodel data is generated from “raw” data captures, such as LIDAR,synthetic aperture radar (SAR), photography, electro-optical, infrared,etc., using systems such as the above-noted RealSite™ and LiteSite™ sitemodeling products, as will be appreciated by those skilled in the art.The geospatial model data set may be generated by another source andprovided to the processor 32 for the additional processing operations tobe described below, or the processor may generate the geospatial modeldata set in other embodiments.

By way of background, with typical prior art approaches, whenautomatically generating a 3D site model from a digital elevation model(DEM), for example, there is a usually a need for a manual (i.e., humanoperator) touch-up due to factors such as: noisy data; occlusion;boundary conditions (being partially cut off, etc.), algorithmlimitations, etc. However, manual touch-up of site models, which can bevery large, may be extremely time consuming. Moreover, locating theareas that require editing in large models may also be tedious anddifficult.

The same issue presents itself in manual site model creation. Manualediting is a relatively long and expensive step in the productionprocess, as a modeler (i.e., the user or operator) often has to render amodel in 3D, locate the problem areas, find where these correlate to in2D space, and then make the corrections in 2D image space. Inparticular, both manual model generation and manual touch-up ofautomated models typically rely upon images to produce a polygon. Thepolygon's height is obtained by calculation based upon cues in the imageor relocating the polygon to another image. Location of the areas to befixed is typically done completely by the modeler (i.e., “by eye”), andis dependent on his/her attention to detail. In other words, thisintroduces the possibility for user error.

As such, in both automated and manual processes, being able to find suchproblem areas in the model, and then having a relatively fast andeffective approach to correct them with little or reduced operatoreffort may save a significant amount of time and cost. Therefore, inaccordance with one aspect, the system 30 may advantageously performenhanced error detection and correction operations.

More particularly, beginning at Block 70, the processor 32advantageously cooperates with the geospatial model data storage device31 for identifying a plurality of localized error regions within ageospatial model data set, at Block 71, and calculates an overall errorvalue for the DEM 40′, at Block 72. An exemplary DEM 40 with errorstherein is shown in FIG. 2. An alternative error view 40′ of the DEM isshown in FIG. 3, and specific localized error regions 41′ within theerror DEM are shown in FIG. 4. That is, the processor 30 advantageouslyseparates areas within the DEM 40 having a relatively high error valuefrom areas having a relatively low error value to determine thelocalized-error regions 41′. By way of example, the error values oflocalized error regions 41′ within the DEM 40, as well as the overallDEM error value, may be calculated using various approaches, including atotal error, local root mean square error (RMSE), a maximum error, amean square error (MSE), MSE relative to the overall DEM, RMSE relativeto the overall DEM, etc., as will be appreciated by those skilled in theart.

${M\; S\; E} = \frac{\sum\limits_{i = 1}^{n}\left( {e_{i} - a_{i}} \right)^{2}}{n}$${R\; M\; S\; E} = \sqrt{M\; S\; E}$${M\; S\; E_{Group}} = \frac{\sum\limits_{{i = 1},{i \in {Group}}}^{n}\left( {e_{i} - a_{i}} \right)^{2}}{n}$${R\; M\; S\; E_{Fixed}} = \sqrt{{M\; S\; E_{DEM}} - {\sum\limits_{{Group} \in {Fixed}}{{MS}\; E_{Group}}}}$

The processor 32 further inpaints one or more of the localized errorregions 41′ to repair or otherwise correct missing, obscured, etc.,portions thereof, at Block 73. More particularly, this may be done bypropagating contour data from outside a given localized error region 41′into the region, as will be appreciated by those skilled in the art. Byway of example, this may be done using various approaches such as aninpainting algorithm, and, more particularly, fluid-flow modelingalgorithms such as Navier-Stokes equations, etc.

Another approach is to perform exemplar inpainting, which involves“cutting” and “pasting” of patches from within the DEM 40 (or adifferent data set) to provide a best match for the corrupted or voideddata, as will be appreciated by those skilled in the art. The inpaintingmay be done in an iterative fashion in some embodiments, as will also beappreciated by the skilled artisan. Further details regarding exemplaryinpainting approaches which may be used are set forth in co-pending U.S.patent application Ser. Nos. 11/458,811 and 11/858,247, which are bothassigned to the present Assignee and are hereby incorporated herein intheir entireties by reference.

The localized error region or regions 41′ to be inpainted may beselected in various ways. In accordance with the exemplary embodimentillustrated in FIG. 8, the localized error regions 41, are prioritizedfor inpainting based upon their respective error values (Block 80′).That is, upon calculating the errors for the localized error regions41′, these regions are prioritized for inpainting based thereon. Forexample, the errors may be sorted by maximum error, relative error,etc., and the regions 41′ are then selected for inpainting one at a timefrom a highest to a lowest error value, as will be appreciated by thoseskilled in the art. However, it should be noted that in otherembodiments the order of regions 41′ to be inpainted could be selectedin a different order, or more than one region could be painted at atime.

The processor 32 re-calculates the overall error value for the DEM 40′after inpainting of the localized error region(s) 41′ to determine ifthe overall error value is below an error threshold, at Blocks 74-75. Ifit is, then the processor 32 stops inpainting of the current localizederror region 41′, thus concluding the method illustrated in FIG. 7, atBlock 76. Otherwise, the processor 32 returns to inpainting of the sameor a different region 41′ until the overall error value is brought belowthe error threshold.

In one exemplary embodiment where localized error regions are inpaintedone at a time from highest to lowest error value, if upon re-calculationof the overall error value the overall error value is not below theerror threshold, then the processor 32 determines whether the errorvalue for the localized error region being inpainted is below an errorthreshold (which may be the same or a different threshold than theoverall threshold), at Block 81′. If it is not, then the processor 32returns to this same localized error region 41′ for more inpaintingoperations. Otherwise, the processor 32 moves to the next localizederror region 41′ (i.e., the one with the next highest error value inline to be inpainted), at Block 82′.

The foregoing will be further understood with reference to the exampleillustrated in FIG. 4. Here, there are seven identified error regions41′ having error values ranging from 1.72018 m RMSE (highest) to 1.58867m RMSE (lowest), and an overall error value for the error DEN 40′ is1.81043 m. The error threshold selected for this example is 1.6 m, butit should be noted that other error thresholds may be used in otherembodiments as appropriate. Accordingly, the processor 32 will firstselect the localized error region 41′ with the 1.72018 m RMSE errorvalue for inpainting, and then inpaint this region until its error valueof the DEM 41′ is less than 1.6 m RMSE, or until the overall error valueis less than 1.6 m RMSE. If the former occurs before the latter, theprocessor 32 moves along to inpaint the next error region 41′ with the1.67647 m RMSE, etc., until the overall error value of the error DEM 40′is less than 1.6 m RMSE.

Another particularly advantageous feature of the system 30 is that oncethe localized error regions 41′ are selected, the processor 32 mayoptionally display the geospatial model data set along with errorindicating boundaries 46 which identify errant buildings 45 or otherobjects/areas on the display 33, at Block 83′, as seen in the TIN 40A ofFIG. 5A (without boundaries), and TIN 40B of FIG. 5B (with boundaries).This allows a user to better visualize exactly which buildings 45, etc.are problem spots within the geospatial model data set. In theillustrated example, the error indicating boundaries 46 are geometricshapes. Moreover, these shapes are transparent or semi-transparent toallow the object with which the indicator is associated to still be seentherethrough. In the illustrated example, the geometric shapes arecylinders or semi-cylindrical shapes (i.e., partial cylinders), whichprovides a desirable visual contrast to generally rectangular buildings.However other shapes/indicators may also be used. The indicators 46 mayalso be colored in certain embodiments to indicate the severity of theerror (e.g., red-orange-yellow-white to indicate highest to lowest errorvalues).

Certain advantages of the above-described system 30 and method are thatthey provide automated model problem area location and prioritization.In some implementations, this approach may be fully automated with nomanual (i.e., user) searching required for problem areas within ageospatial model data set. Moreover, the results may helpfully beprioritized by which areas should be addressed first.

In accordance with another aspect now described with reference to FIGS.9-12, a system 30′ and associated method for helping a user to moreeasily replace buildings 45 within localized error regions 41′ of ageospatial model data set is now described. Beginning at Block 110, theprocessor 32′ cooperates with the model data storage device 31′ anddisplay 33, to display geospatial model data including one or moregroups of data points corresponding to a respective building 45, atBlock 111. A given group of building data points may be selected forprocessing, such as by automatic selection of a group of points by theprocessor 32′ or manual selection by a user with a user input device34′, which may be a mouse, joystick, keyboard, etc., as will beappreciated by those skilled in the art.

In one exemplary automated embodiment, a queue may be constructed usingthe above-described localized error region 41′ error valueprioritization (i.e., based upon error calculation) to create a queuefor buildings that need to be corrected, at Block 120′, and theprocessor 32′ may take these in the order they are in the queue (e.g.,from highest error value to lowest error value). The error calculationsmay be performed using the above-described approaches (e.g., RMSE,etc.), for example, as discussed further above. Moreover, these errorcalculations may also advantageously be performed on either 2D or 3Ddata sets, as will be appreciated by those skilled in the art.Alternatively, upon display of the error indicating boundaries/shapes46, a user may manually select (with the user input device 34′) adesired group of building data points to be corrected. Other suitableselection approaches may be used as well, as will be appreciated bythose skilled in the art.

It should be noted that, as used herein, “3D” is meant to cover bothtrue three-dimensional model data as well as so-called 2½ or 2.5D modeldata. More specifically, many DEMS or other geospatial model data setsare sometimes referred to as “2.5D” because they include renderedbuilding walls, etc. that are not necessarily present in the originaldata capture, and thus do not provide a completely accurate 3D image asit would appear to the human eye upon viewing a scene. However, forclarity of discussed “3D” is meant to cover both cases herein.

For the selected group of building data points, the processor 32′ thenadvantageously displays a plurality of different user-selectablebuilding shapes 100 a-100 d (FIG. 10), at Block 112. That is, theprocessor 32′ presents the user with a plurality of possible buildingshapes, so that the user can quickly select a shape that best fits theselected group of points. In particular, the plurality ofuser-selectable different building shapes 100 a-100 d have differentrespective feature detail levels associated therewith.

Generally speaking, the user decides which building shape 100 a-100 d toselect based upon a tradeoff between visual resemblance and acceptableerror for each building 45, which will depend upon the particular errorparameters for a given geospatial model data set, as will be appreciatedby those skilled in the art. Once a desired building shape has beenselected by the user (i.e., with the user input device 34′), theprocessor 32′ may then advantageously replace the given group ofbuilding data points with the selected building shape, at Block 113, andmay update the data set accordingly (i.e., save the change in the modeldata storage device 31′), thus concluding the method illustrated in FIG.11 (Block 114).

In the illustrated example, the shape with the least or lowest featuredetail level (and, correspondingly, the highest error value associatedtherewith) is the generally rectangular building or “bounding” box 100a. On the other hand, the shape 100 d has the highest feature detaillevel (i.e., the lowest error value), because it is a one-to-one matchof the group of building data points. That is, the shape 100 d includesall of the detail present in the original data set. The shapes 100 b and100 c have varying levels of detail between the highest and lowestlevels of the shapes 100 a and 100 d, respectively, as seen in FIG. 10.

The plurality of user-selectable building shapes 100 a-100 d mayconceptually be considered as a “toolbox” of possible building shapesfrom which the user can quickly select a given shape to more accuratelyreflect the true or “real-life” shape of the actual building 45 beingrendered in the model. This toolbox of shapes 100 a-100 d may be used inlieu of inpainting the building as described above. That is, using theabove described approach, the processor 32′ may select groups ofbuilding data points to be corrected in order based upon error valuesassociated therewith, and then present the user with respective buildingshapes for each building to replace the errant groups of data pointsuntil the overall error of the geospatial model data set falls below theerror threshold (or the localized error threshold falls below the errorthreshold). Of course, in other embodiments the user may just manuallyreplace desired groups of building points without the use of errorthresholds, if desired.

In addition, multiple levels of building shapes or toolboxes may bepresented to the user. For example, the user may be presented with thefirst set of four building shapes 100 a-100 d, and based upon theselected shape the processor 32′ may provide another set of four shapeswith detail levels (or error values) closer to the first selected shape,etc. Thus, the user can “drill-down” through a plurality of shape levelsto obtain a most desired match. Moreover, through each successive levelof potential shapes, the processor 32′ may add further details orfeatures to the shapes, such as roof pitches, etc. This mayadvantageously help conserve processing time, since the processor 32′does not have to generate multiple shapes along a large spectrum ofpossible error values, but rather can quickly focus in on a narrowererror range of interest to the user and only generate building shapeswithin this range, as will be appreciated by those skilled in the art.

Certain advantages of the system 30′ and associated methods are thatthey provide automated problem resolution, including a relatively fullrange of automatically created options (i.e., user-selectable buildingshapes) that are presented to a user on screen, ranging from thebuilding box 100 a all the way to the original point cloud 100 d.Moreover, this approach may advantageously allow users to makecustomized solutions “on the fly” based on user requirements.

Turning additionally to FIGS. 13-17, another advantageous geospatialmodeling system 30″ and related method aspect are now described whichallow a user to relatively quickly and easily perform manual errorcorrection on a 3D model, rather than having to translate over to 2Dimage space. By way of example, the following approach may be used incombination with the above-described methods such that if a localizederror region cannot be inpainted to the point where its error value isbelow the error threshold, or a suitable building shape cannot bepresented for the user, the user can “manually” correct an errantobject, such as a building. It should be noted that reference herein toa “building” is meant to include a wide range of man-made structures(e.g., houses, stadiums, arenas, storage tanks, bridges, etc.), and notjust high-rise building structures, although “building” is usedthroughout for simplicity and clarity of reference.

More particularly, beginning at Block 170, in the present embodiment theprocessor 32″ cooperates with the geospatial model data storage device31″, the user input device 34″, and the display 33″ for displaying a 3Dgeospatial model data set on the display, at Block 171. Moreover, abuilding boundary 141 is displayed around a user-selected building areain a DEM 140 responsive to the user input device 34″ (Block 172), and ahistogram of height values within the selected building area isgenerated, at Block 173. In the present example, the DEM 140 is “raw” inthat no error correction has yet been performed thereto.

The processor 32″ may then advantageously determine a building heightbased upon the histogram of height values, at Block 174. By way ofexample, the building height may be based upon a peak histogram value.More particularly, the building height may be chosen as the center of afirst or predominant height cluster (starting from a minimum value ofthe histogram) that meets a predetermined set of statistical criteria.This approach may advantageously select a building height with lesssusceptibility to error from noisy edge posts for data collection pointsaround the edges of buildings, for example, as will be appreciated bythose skilled in the art.

Once the building height is determined, a building shape is thengenerated based upon the user-selected building area 141 and thedetermined building height, at Block 175, and data points within theuser-selected building area are replaced based upon the building shape,at Block 176, thus concluding the illustrated method (Block 177). Thatis, the processor 32″ renders a new building shape having the outline ofthe user-selected building area 141 and the determined building height.

In FIG. 15, the DEM 140′ is first shown (left-hand side of drawing)after the original input was run through the LiteSite® AutomatedBuilding Vector process. The DEM 140′ includes areas with shortbuildings, boundary conditions/errors, occlusions, and noise, forexample.

By way of comparison, a manually touched-up version of the DEM 140″using the above-noted approach is shown on the right-hand side of FIG.15. This corrected DEM 140″ took approximately two minutes of user timeto perform the error correction operations shown (by a trainedoperator), above the requisite processing (i.e., CPU) time. However,this CPU processing time is relatively insignificant compared to theprocessing time required for the fully automated error correction. FIGS.16A and 16B are corresponding simulated 3D views 140A′ and 140B′ of theautomatically corrected DEM 140′ and manually corrected DEM 140″ of FIG.15, respectively. As seen in the simulated views, the manual touch-upresults in the generation of buildings 145B′ not included in the fullyautomated DEM 140′ (i.e., in addition to buildings 145A′ presenttherein).

Depending upon a given implementation and the particular error/accuracyrequirements for a particular geospatial model data set, it may beacceptable to simply use a fully automated approach to error correction,as this provides significant improvement over the basic uncorrected DEM140. However, where further accuracy is required, the above-described 3Dmanual error correction approach (which is referred to herein as“manual” because it requires some user involvement to draw theuser-selected building area 141, although the remainder of the processmay in fact be automated) may advantageously be used in place of, or asa supplement to, fully automated error correction. It is noteworthy thatthe above-described manual approach is much less time consuming thantypical current manual model editing tools, which require translation orre-location to corresponding 2D views, user guessing of building heightvalues, etc., which can be very time consuming for the user.

Accordingly, the system 30″ and associated methods may advantageouslyallow for drawing directly on a DEM, etc., as an additional qualitycontrol option. This approach does not require user involvement toobtain the height of a manually drawn building, i.e., the processor 32″advantageously does this for the user in an automated fashion based uponthe user-selected building area 141. As such, time-consuming relocationto 2D images need not be performed, as with many prior art image-basederror correction techniques. Moreover, since problem areas may becorrected directly on the displayed 3D DEM or other 3D data model,additional satellite or aerial reference imagery need not be required,which can provide additional time and cost savings.

Other advantages of the above-described systems and methods may include:savings of time and cost in the quality control phase of 3D modelingproduction; making automated 3D model creation more flexible andverifiable to a given set of model requirements; potentially decreasingthe chance of sending out a final product with a problem areaoverlooked; and decreasing re-work of generated models.

The above-noted geospatial model method, aspects may also be embodied ina computer-readable medium having computer-executable instructions forcausing a computer to perform the steps set forth above, as will beappreciated by those skilled in the art.

Additional features of the invention may be found in co-pendingapplications entitled GEOSPATIAL MODELING SYSTEM PROVIDING INPAINTINGAND ERROR CALCULATION FEATURES AND RELATED METHODS, Ser. No. 11/863,377;and GEOSPATIAL MODELING SYSTEM PROVIDING BUILDING GENERATION BASED UPONUSER INPUT ON 3D MODEL AND RELATED METHODS, Ser. No. 11/863,417, theentire disclosures of which are hereby incorporated herein by reference.

Many modifications and other embodiments of the invention will come tothe mind of one skilled in the art having the benefit of the teachingspresented in the foregoing descriptions and the associated drawings.Therefore, it is understood that the invention is not to be limited tothe specific embodiments disclosed, and that modifications andembodiments are intended to be included within the scope of the appendedclaims.

That which is claimed is:
 1. A geospatial modeling system comprising: ageospatial model data storage device; a user input device; a display;and a processor cooperating with said geospatial model data storagedevice, said user input device and said display and configured todisplay a geospatial model data set on said display including at leastone group of building data points, display a plurality ofuser-selectable different building shapes on said display based upon theat least one group of building data points, the plurality ofuser-selectable different building shapes having different respectivefeature detail levels, and each feature detail level having a differentrespective error value associated therewith, and replace the at leastone group of building data points with a given one of theuser-selectable different building shapes based upon user selectionthereof with said user input device.
 2. The geospatial modeling systemof claim 1 wherein said processor is further configured to update thegeospatial model data based upon the user selection of the given one ofthe user-selectable different building shapes.
 3. The geospatialmodeling system of claim 1 wherein the plurality of user-selectabledifferent building shapes comprises a generally rectangular building boxas a lowest feature detail level.
 4. The geospatial modeling system ofclaim 1 wherein the plurality of user-selectable different buildingshapes comprises a match of the at least one group of building datapoints as a highest feature detail level.
 5. The geospatial modelingsystem of claim 4 wherein the plurality of user-selectable differentbuilding shapes further comprises a generally rectangular building boxas a lowest feature detail level, and a plurality of user-selectablebuilding shapes having respective different intermediate feature detaillevels between the lowest and highest feature detail levels.
 6. Thegeospatial modeling system of claim 1 wherein said processor is furtherconfigured to select for displaying the at least one group of buildingpoints from among a plurality of groups thereof based upon errorcalculations.
 7. The geospatial modeling system of claim 6 wherein theerror calculations comprise at least one root mean square error (RMSE)calculation.
 8. The geospatial modeling system of claim 6 wherein theerror calculations comprise at least two-dimensional error calculation.9. The geospatial modeling system of claim 6 wherein the errorcalculations comprise at least three-dimensional error calculation. 10.A geospatial modeling system comprising: a geospatial model data storagedevice; a user input device; a display; and a processor cooperating withsaid geospatial model data storage device, said user input device andsaid display and configured to display a geospatial model data set onsaid display including at least one group of building data points,display a plurality of user-selectable different building shapes on saiddisplay based upon the at least one group of building data points, theplurality of user-selectable different building shapes comprising amatch of the at least one group of building data points as a highestfeature detail level, a generally rectangular building box as a lowestfeature detail level, and a plurality of user-selectable building shapeshaving respective different intermediate feature detail levels betweenthe lowest and highest feature detail levels, and each feature detaillevel having a different respective error value associated therewith,replace the at least one group of building data points with a given oneof the user-selectable different building shapes based upon userselection thereof with said user input device, and update the geospatialmodel data based upon the user selection of the given one of theuser-selectable different building shapes.
 11. The geospatial modelingsystem of claim 10 wherein said processor is further configured toselect for displaying the at least one group of building points fromamong a plurality of groups thereof based upon error calculations. 12.The geospatial modeling system of claim 11 wherein the errorcalculations comprise at least one root mean square error (RMSE)calculation.
 13. A geospatial modeling method comprising: using aprocessor to display a geospatial model data set on a display includingat least one group of building data points; using a processor to displaya plurality of user-selectable different building shapes on the displaybased upon the at least one group of building data points, the pluralityof user-selectable different building shapes having different respectivefeature detail levels, and each feature detail level having a differentrespective error value associated therewith; and using a processor toreplace the at least one group of building data points with a given oneof the user-selectable different building shapes based upon userselection thereof with a user input device.
 14. The geospatial modelingmethod of claim 13 further comprising using the processor to update thegeospatial model data based upon the user selection of the given one ofthe user-selectable different building shapes.
 15. The geospatialmodeling method of claim 13 wherein the plurality of user-selectabledifferent building shapes comprises a generally rectangular building boxas a lowest feature detail level.
 16. The geospatial modeling method ofclaim 13 wherein the plurality of user-selectable different buildingshapes comprises a match of the at least one group of building datapoints as a highest feature detail level.
 17. The geospatial modelingmethod of claim 16 wherein the plurality of user-selectable differentbuilding shapes further comprises a generally rectangular building boxas a lowest feature detail level, and a plurality of user-selectablebuilding shapes having respective different intermediate feature detaillevels between the lowest and highest feature detail levels.
 18. Thegeospatial modeling method of claim 13 further comprising using theprocessor to select the at least one group of building points fordisplaying from among a plurality of groups thereof based upon errorcalculations.
 19. A non-transitory computer-readable medium havingcomputer executable instructions for causing a computer to perform stepscomprising: displaying a geospatial model data set on a displayincluding at least one group of building data points; displaying aplurality of user-selectable different building shapes on the displaybased upon the at least one group of building data points, the pluralityof user-selectable different building shapes having different respectivefeature detail levels, and each feature detail level having a differentrespective error value associated therewith; and replacing the at leastone group of building data points with a given one of theuser-selectable different building shapes based upon user selectionthereof with a user input device.
 20. The non-transitorycomputer-readable medium of claim 19 further having computer-executableinstructions for causing the computer to perform a step of updating thegeospatial model data based upon the user selection of the given one ofthe user-selectable different building shapes.
 21. The non-transitorycomputer-readable medium of claim 19 wherein the plurality ofuser-selectable different building shapes comprises a generallyrectangular building box as a lowest feature detail level.
 22. Thenon-transitory computer-readable medium of claim 19 wherein theplurality of user-selectable different building shapes comprises a matchof the at least one group of building data points as a highest featuredetail level.
 23. The non-transitory computer-readable medium of claim22 wherein the plurality of user-selectable different building shapesfurther comprises a generally rectangular building box as a lowestfeature detail level, and a plurality of user-selectable building shapeshaving respective different intermediate feature detail levels betweenthe lowest and highest feature detail levels.
 24. The non-transitorycomputer-readable medium of claim 19 further having computer-executableinstructions for causing the computer to perform a step of selecting theat least one group of building points for displaying from among aplurality of groups thereof based upon error calculations.